# Semiparametric Latent Topic Modeling on Consumer-Generated Corpora

arXiv: https://arxiv.org/abs/2107.10651 · HTML: https://mlsanity.com/paper/2107.10651/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://mlsanity.com/paper/1605.02019/) (2016)
- [Nonparametric Spherical Topic Modeling with Word Embeddings](https://mlsanity.com/paper/1604.00126/) (2016)
- [What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)](https://mlsanity.com/paper/1608.08176/) (2016)

## Abstract (arXiv)

Legacy procedures for topic modelling have generally suffered problems of overfitting and a weakness towards reconstructing sparse topic structures. With motivation from a consumer-generated corpora, this paper proposes semiparametric topic model, a two-step approach utilizing nonnegative matrix factorization and semiparametric regression in topic modeling. The model enables the reconstruction of sparse topic structures in the corpus and provides a generative model for predicting topics in new documents entering the corpus. Assuming the presence of auxiliary information related to the topics, this approach exhibits better performance in discovering underlying topic structures in cases where the corpora are small and limited in vocabulary. In an actual consumer feedback corpus, the model also demonstrably provides interpretable and useful topic definitions comparable with those produced by other methods.